Full-speed-domain position-sensorless control method and system for permanent magnet synchronous motor

Abstract

The invention discloses a permanent magnet synchronous motor full-speed-domain position-sensorless hybrid control method and a permanent magnet synchronous motor full-speed-domain position-sensorless hybrid control system, and the permanent magnet synchronous motor full-speed-domain position-sensorless hybrid control method is constructed by taking a permanent magnet synchronous motor nonlinear dynamic system model as a starting point. A hybrid control method combining high-frequency pulsating square wave signal injection under a linear dynamic system model, a rotor polarity judgment and rotor initial position estimation algorithm and an unscented Kalman filtering extension algorithm in an additive noise form under a nonlinear discrete dynamic system model is adopted; and obtaining the optimal estimation observation value of the system state variable in the static, low-speed, medium-speed and high-speed full-speed domain range under the condition that the permanent magnet synchronous motor is not provided with a mechanical position sensor. The optimal estimation observation value is used for compensating and feeding back a permanent magnet synchronous motor drive control system adjusting strategy, disturbance of load torque is improved, the noise amount and disturbance amount introduced by three-phase current measurement are eliminated, and efficient and accurate control operation of the permanent magnet synchronous motor is achieved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of permanent magnet synchronous motor control, and in particular to a position sensorless control method and system for a permanent magnet synchronous motor in full speed range. Background Art

[0002] As the global energy structure transition accelerates, permanent magnet synchronous motors and their drive systems, with their inherent advantages of high efficiency and zero emissions, have become the core vehicle for achieving the "dual carbon" strategy. In particular, in areas such as new energy vehicles and intelligent connected equipment, permanent magnet synchronous motors have become the mainstream drive solution due to the high power density, high efficiency, and strong overload capacity afforded by rare earth permanent magnet materials. my country boasts abundant rare earth resources, and its permanent magnet material research and development and manufacturing technology has reached internationally advanced levels, laying a strategic foundation for the large-scale application of permanent magnet synchronous motors. In recent years, my country has vigorously developed the strategic new energy vehicle industry. The internal permanent magnet synchronous motor, with its salient-pole rotor structure generating reluctance torque, further enhances its field-weakening speed expansion capability and dynamic response performance, perfectly meeting the stringent requirements of vehicle drive systems for a wide speed range and high transient response, and has been widely used.

[0003] However, the high-performance drive control of permanent magnet synchronous motors has long relied on mechanical position sensors, such as resolvers, photoelectric encoders, or Hall sensors. These sensors not only increase system size and cost, but also introduce additional failure points and electromagnetic compatibility issues, severely limiting system reliability. This is especially true in complex and harsh environments such as vibration, high temperatures, and oil contamination, where the risk of sensor failure increases significantly. To overcome these bottlenecks, position sensorless control technology has become a research hotspot in recent years, but it still faces three major technical barriers across the full speed range:

[0004] (1) Observation failure in the low-speed region. The traditional back-EMF observation method has a low signal-to-noise ratio in the zero-speed and low-speed regions, and the rotor position detection accuracy drops sharply.

[0005] (2) Parameter sensitivity in the medium and high speed domain. The observer based on the motor model is affected by the temperature drift of the resistor and inductor and the magnetic saturation effect, which leads to position estimation drift.

[0006] (3) Insufficient dynamic response in the full speed range. The single position observation method is difficult to take into account both low-speed stability and high-speed dynamic performance, and the switching process is prone to torque pulsation.

[0007] Therefore, how to achieve full-speed range control of permanent magnet synchronous motors without position sensors is an urgent problem to be solved. Summary of the Invention

[0008] The purpose of the present invention is to at least provide a control method and system for a permanent magnet synchronous motor in the full speed range without a position sensor, which can at least solve the technical problem of how to control the full speed range of the permanent magnet synchronous motor in the absence of a position sensor, and at least achieve the goal of obtaining the optimal estimated observation values ​​of the system state variables when the permanent magnet synchronous motor is in the full speed range such as static, low speed, medium speed, and high speed without a mechanical position sensor, and use the optimal estimated observation values ​​to compensate and feedback the drive control system, improve disturbance and noise, and significantly improve the load dynamic adaptability and control accuracy of the system.

[0009] To solve the above technical problems, at least one embodiment of the present application provides a full-speed range position sensorless control method for a permanent magnet synchronous motor, comprising: sampling the three-phase current of the natural coordinate system of the permanent magnet synchronous motor, performing coordinate system conversion to obtain an estimated observation current of the synchronous rotating coordinate system, and determining an initial estimated rotor position angle under the high-frequency pulse square wave signal injection strategy based on the observed current; determining an initial estimated rotor position polarity based on the high-frequency current response amplitude under the high-frequency current injection; determining an estimated rotor position angle based on the rotor position polarity and the initial estimated rotor position angle; using an unscented Kalman filter extended algorithm in the form of additive noise, taking the initial position of the estimated rotor position angle as input, to obtain an optimal estimated observation value of the system state, the optimal estimated observation value of the system state including the optimal estimated observation value of the rotor position angle; fusing the estimated rotor position angle and the optimal estimated observation value of the rotor position angle to obtain a final estimated rotor position angle, and feeding the final estimated rotor position angle and the optimal estimated observation value of the system state into a permanent magnet synchronous motor drive control system regulation system to improve the control of the permanent magnet synchronous motor.

[0010] At least one embodiment of the present application further provides a full-speed range position sensorless control system for a permanent magnet synchronous motor, comprising: a nonlinear dynamic hybrid component and an operation control observation and regulation component;

[0011] The operation control observation and adjustment component includes: a high-frequency pulse square wave signal injection module, an unscented Kalman filter extended algorithm module in the form of additive noise, and a fusion module. The high-frequency pulse square wave signal injection module and the unscented Kalman filter extended algorithm module in the form of additive noise are respectively connected to the fusion module, and are used to adopt the full-speed domain control method as described in the application to output a final estimated rotor position angle and an optimal estimated observation value of the system state, determine the rotor position based on the final estimated rotor position angle, and improve the parameters of the drive control system adjustment strategy based on the optimal estimated observation value of the system state, so as to control the operation of the permanent magnet synchronous motor without a position sensor within the full speed domain;

[0012] A fusion module is used to fuse the estimated rotor position angle output by the high-frequency pulse square wave signal injection module and the optimal estimated observation rotor position angle output by the unscented Kalman filter extended algorithm module in the form of additive noise, and obtain the final estimated rotor position angle;

[0013] An unscented Kalman filter extended algorithm module in the form of additive noise is used to output an optimal estimated observation value of the system state based on the current of the permanent magnet synchronous motor in the rotating coordinate system and the high-frequency current of the permanent magnet synchronous motor in the synchronous rotating coordinate system;

[0014] The nonlinear dynamic hybrid component includes a drive inversion module, which is used to correct the drive parameters of the permanent magnet synchronous motor according to the optimal estimated observation value of the system state and output a drive signal to the permanent magnet synchronous motor.

[0015] At least one embodiment of the present application also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the full-speed domain control method of the present application.

[0016] The embodiments of the present application provide a full-speed range position sensorless control method and system for a permanent magnet synchronous motor. Starting from the construction of a nonlinear dynamic system model of the permanent magnet synchronous motor, a control method combining high-frequency pulse square wave signal injection with an unscented Kalman filter extension algorithm in the form of additive noise is used to obtain the optimal estimated observation value of the permanent magnet synchronous motor system state variables and the final estimated rotor position angle, thereby achieving smooth switching and high-precision control in the full speed range of stationary, low speed, medium speed, and high speed without a mechanical position sensor. There is no need to use a mechanical position sensor, thereby effectively improving the reliability and environmental adaptability of the drive system.

[0017] In some optional embodiments, the high-frequency pulse square wave signal injection method is used to determine the initial estimated rotor position angle, including: establishing a linearized high-frequency voltage model in an estimated synchronous rotating coordinate system, and obtaining a high-frequency current based on the observed current; estimating the relationship between the q-axis current transformation amount and the initial estimated rotor position angle error in the estimated synchronous rotating coordinate system based on the current difference between the measurement points before and after each high-frequency pulse square wave signal injection, to obtain an error signal, and converging the error signal to obtain the initial estimated rotor position angle.

[0018] A high-frequency pulse square wave signal injection method is used to obtain the initial estimated rotor position angle, which serves as the main parameter basis source of the permanent magnet synchronous motor when it is stationary or at low speed, and provides a basis for control when it is stationary or at low speed.

[0019] In some optional embodiments, the use of the high-frequency current response amplitude under high-frequency current injection to determine the initial estimated rotor position polarity includes: defining inductance parameters under positive and negative d-axis currents; calculating the impact of dq-axis incremental self-inductance changes on positive-sequence inductance and high-frequency current, and determining the polarity of the initial estimated rotor position angle based on the absolute value of the high-frequency current response amplitude when the positive d-axis current is injected and the absolute value of the high-frequency current response amplitude when the negative d-axis current is injected.

[0020] By determining the polarity of the initial estimated rotor position angle, it is determined whether the initial estimated rotor position angle needs to be corrected, thereby ensuring that the estimated rotor position angle is accurate.

[0021] In some optional embodiments, the unscented Kalman filter extended algorithm using additive noise obtains the optimal estimated observation value of the system state based on the estimated initial position of the rotor position angle, including: establishing a nonlinear discrete dynamic system model of the permanent magnet synchronous motor, using the estimated initial value of the rotor position angle as the initial input, initializing the filter parameters, obtaining the Sigma point set and its weight coefficient at the previous moment, updating the time of the nonlinear discrete dynamic system model, updating the observation equation, obtaining the one-step observation value of the system state, correcting the prediction vector, and obtaining the optimal system state variable observation value.

[0022] An extended unscented Kalman filter algorithm is employed, with the estimated initial rotor position angle as the initial input to the algorithm. The filter parameters are initialized, and the estimated rotor position angle obtained by injecting a high-frequency pulsed square wave signal at standstill or low speed is used as the primary value. At medium and high speeds, the optimal estimated rotor position observation value obtained by the extended unscented Kalman filter algorithm in the form of additive noise is used as the primary value. These observations are then integrated to achieve smooth switching and high-precision control between standstill or low speed and medium and high speeds, eliminating the need for mechanical position sensors and effectively improving the reliability and environmental adaptability of the drive system. Feedback of the optimal system state variable improves load torque disturbances, eliminates noise and disturbances introduced by current measurement, and significantly enhances the system's load dynamic adaptability and control accuracy.

[0023] In some optional embodiments, the establishment of a nonlinear discrete dynamic system model of a permanent magnet synchronous motor is as shown in the following equation:

[0024]

[0025] Where, Indicates the reference input voltage, y k =[i d,k i q,k ] T Represents the observation output, w k-1 represents the system noise, w c,k-1 represents process noise, v krepresents the observation noise, f(x k-1 ,u k-1 ,w c,k-1 ) represents the system dynamic matrix, h(x k ) represents the output matrix, K represents the time, x=[i d i q ωθC ch ] T Represents system state variables.

[0026] In the dynamic system model, system noise, process noise and observation noise are added to the system state variables to provide an analytical basis for achieving precise control of the permanent magnet synchronous motor by the system.

[0027] In some optional embodiments, fusing the estimated rotor position angle and the optimal estimated observation rotor position angle to obtain the estimated rotor position angle includes: a position fusion strategy of the estimated rotor position angle and the optimal estimated observation rotor position angle is shown in the following formula:

[0028]

[0029] In the formula, μ represents the weighting factor, which is calculated as follows:

[0030]

[0031] in, is the optimal estimated speed, ω low is the low-speed boundary of the switching area, ω high It is the high-speed boundary of the switching area.

[0032] The estimated rotor position angle is fused with the optimal estimated observed rotor position angle, achieving smooth switching between the high-frequency pulse square wave signal injection method and the unscented Kalman filter extended algorithm in the form of additive noise, ensuring the control of the permanent magnet synchronous motor without a position sensor in the full speed range.

[0033] In some optional embodiments, the high-frequency pulse square wave signal injection module includes: a linearized high-frequency motor model unit, a demodulation unit, a position observer unit, a polarity judgment unit and an adjustment unit. The linearized high-frequency motor model unit, the demodulation unit and the position observer unit are connected in sequence, and the position observer unit and the polarity judgment unit are respectively connected to the adjustment unit; the linearized high-frequency motor model unit is used to sample the three-phase current of the permanent magnet synchronous motor based on the linearized high-frequency motor model and the high-frequency pulse square wave injection signal, and perform coordinate system conversion to obtain an estimated observation current in the synchronous rotating coordinate system, and obtain the high-frequency current after filtering the observation current; the demodulation unit is used to obtain the high-frequency current according to the difference between the measurement points before and after each high-frequency pulse square wave signal injection. The current difference is used to estimate the relationship between the estimated q-axis current transformation amount in the synchronous rotating coordinate system and the initial estimated rotor position angle error to obtain an error signal; a position observer unit is used to adjust the error signal to zero to obtain the initial estimated rotor position angle after convergence without polarity determination; a polarity judgment unit is used to calculate the high-frequency current response amplitude of the injected positive d-axis current and the high-frequency current response amplitude of the injected negative d-axis current, and obtain the polarity of the estimated rotor position angle based on the comparison of the absolute value of the high-frequency current response amplitude of the injected positive d-axis current and the absolute value of the high-frequency current response amplitude of the injected negative d-axis current; an adjustment unit is used to obtain the estimated rotor position angle based on the polarity of the initial estimated rotor position angle after convergence without polarity determination and the estimated rotor position angle.

[0034] According to the high-frequency pulse square wave injection signal, the observed current is obtained as the basis of the operation. The error signal is obtained through the current flow before and after each high-frequency pulse square wave signal injection. The error signal is converged to obtain the initial estimated rotor position angle. The polarity of the estimated initial rotor position angle is then calculated. According to the polarity and the initial estimated rotor position angle, the estimated rotor position angle is determined, thereby ensuring the accuracy of the rotor position angle and improving the accuracy of the permanent magnet synchronous motor operation control.

[0035] In some optional embodiments, the unscented Kalman filter extended algorithm module in the additive noise form includes: a nonlinear discrete dynamic system model unit and a filter extension unit connected in sequence;

[0036] The nonlinear discrete dynamic system model unit is used to consider the influence of various types of noise and incorporate the expansion of system state variables. Various types of noise include process evolution disturbance noise, additive noise, and observation noise. The first-order discretized state equation is established, and the nonlinear discrete dynamic system model is constructed based on the first-order discretized state equation and the dynamic matrix equation.

[0037] The filter extension unit is used to perform an unscented Kalman filter extension algorithm on the nonlinear discrete dynamic system model to obtain the optimal estimate of the state variable.

[0038] A nonlinear discrete dynamic system model unit is used to integrate various types of noise into the system state variables, expand the system state variables, fully consider the impact of different types of noise on the system, expand the range of system state variables, and construct a nonlinear discrete dynamic system model based on the discretized state equation to fully analyze the medium and high-speed operation of the system; the filtering expansion unit, based on the estimated initial value of the rotor position angle as the filtering initial value, uses the unscented Kalman filter extension algorithm to filter the nonlinear discrete dynamic system model to obtain the optimal estimated observation value of the system state variable, which is used to accurately control the operation of the permanent magnet synchronous motor. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] One or more embodiments are exemplarily described by the figures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments.

[0040] Figure 1 This is a schematic diagram of the principle of a full-speed range control system provided by an embodiment of the present application;

[0041] Figure 2 This is a schematic diagram of the principle of a rotor position angle estimation fusion module provided by an embodiment of the present application;

[0042] Figure 3 This is a flow chart of an extended algorithm for an unscented Kalman filter in the form of additive noise provided by one embodiment of the present application;

[0043] Figure 4 This is a flow chart of a method for estimating the polarity of the rotor position angle using the d-axis current injection method provided by an embodiment of the present application;

[0044] Figure 5 This is a schematic diagram of the principle of an optimal drive control system adjustment strategy provided by an embodiment of the present application;

[0045] Figure 6 This is a schematic diagram of the principle of the drive control system adjustment strategy using the operating intention as the target operating point provided by another embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, each embodiment of the present application will be described in detail below with reference to the accompanying drawings. However, it will be understood by those skilled in the art that in each embodiment of the present application, many technical details are proposed to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solutions claimed in the present application can be implemented. The division of the following embodiments is for convenience of description and should not constitute any limitation on the specific implementation of the present application. The various embodiments can be combined and referenced with each other under the premise of no contradiction.

[0047] In order to solve the technical problem that a permanent magnet synchronous motor cannot achieve full-speed control when there is no position sensor, the present invention proposes a control method for a permanent magnet synchronous motor in the full-speed range without a position sensor. The implementation details of a control method for a permanent magnet synchronous motor in the full-speed range without a position sensor in this embodiment are described in detail below. The following content is only the implementation details provided for the convenience of understanding and is not necessary for the implementation of this solution.

[0048] Example 1:

[0049] The present embodiment provides a permanent magnet synchronous motor full speed range position sensorless control system that can be applied to electrical or electronic equipment with communication, computing and data storage capabilities, such as Figure 1 Shown, including:

[0050] Linear dynamic hybrid component and operation control observation and regulation component;

[0051] The operation control observation and adjustment component includes: a high-frequency pulse square wave signal injection module, an unscented Kalman filter extended algorithm module in the form of additive noise, and a fusion module. The high-frequency pulse square wave signal injection module and the unscented Kalman filter extended algorithm module in the form of additive noise are respectively connected to the fusion module.

[0052] The high-frequency pulse square wave signal injection module is used to perform coordinate transformation based on the three-phase current of the permanent magnet synchronous motor to obtain the observed current in the estimated synchronous rotating coordinate system, and adopt the high-frequency pulse square wave signal injection method to obtain the initial estimated rotor position angle; by injecting high-frequency direct current on the d-axis, the initial estimated rotor position angle polarity is obtained according to the absolute value of the positive d-axis injection current response amplitude and the absolute value of the negative d-axis injection current response amplitude, and the estimated rotor position angle is obtained according to the initial estimated rotor position angle and the initial estimated rotor position angle polarity.

[0053] The extended algorithm module of the unscented Kalman filter in the form of additive noise is used to incorporate the influence of various types of noise into the system state variables, establish a nonlinear discrete dynamic system model of the permanent magnet synchronous motor, take the estimated initial position of the rotor position angle as input, filter the nonlinear discrete dynamic system model with the extended algorithm of the unscented Kalman filter, and output the optimal estimated observation value of the system state, which includes the optimal estimated current value. and Optimal estimated load torque Optimal estimated observation rotor position angle is the optimal estimated speed.

[0054] The optimal estimated observation value of the system state is used to improve the parameters of the drive control system adjustment strategy and control the permanent magnet synchronous motor to operate in the full speed range without a position sensor.

[0055] The fusion module is used to perform position fusion on the estimated rotor position angle output by the high-frequency pulse square wave signal injection module and the optimal estimated observation rotor position angle output by the unscented Kalman filter extended algorithm module in the form of additive noise, and obtain the final estimated rotor position angle for determining the rotor position.

[0056] The nonlinear dynamic hybrid component includes a drive inverter module, which includes a drive inverter module. The drive inverter module includes a drive system of a permanent magnet synchronous motor, which is used to determine the rotor position based on the final estimated rotor position angle, correct the drive parameters of the permanent magnet synchronous motor based on the optimal estimated observation value of the system state, and output a drive signal to the permanent magnet synchronous motor.

[0057] like Figure 2 As shown, the high-frequency pulse square wave signal injection module includes: a linearized high-frequency motor model unit, a demodulation unit, a position observer unit, a polarity judgment unit and an adjustment unit. The linearized high-frequency motor model unit, the demodulation unit, and the position observer unit are connected in sequence, and the position observer unit and the polarity judgment unit are respectively connected to the adjustment unit;

[0058] The linearized high-frequency motor model unit is used to establish a linearized high-frequency motor model and sample the three-phase current i in the natural coordinate system of the permanent magnet synchronous motor according to the high-frequency pulse square wave injection signal. a 、i b 、i c , and perform coordinate system transformation to obtain the estimated observed current in the synchronous rotating coordinate system, and obtain the high-frequency current after filtering the observed current.

[0059] The demodulation unit is used to estimate the relationship between the estimated q-axis current transformation amount in the synchronous rotating coordinate system and the initial estimated rotor position angle error based on the current difference between the measurement points before and after each high-frequency pulse square wave signal is injected, and obtain an error signal.

[0060] The position observer unit is used to adjust the error signal to zero to obtain an initial estimated rotor position angle after convergence without polarity determination.

[0061] a polarity determination unit, configured to calculate a response amplitude of the positive d-axis high-frequency DC current and a response amplitude of the negative d-axis high-frequency DC current, compare the absolute value of the response amplitude of the positive d-axis high-frequency DC current with the absolute value of the response amplitude of the negative d-axis high-frequency DC current, and obtain the polarity of the initial estimated rotor position angle;

[0062] The adjustment unit is used to obtain the estimated rotor position angle according to the converged initial estimated rotor position angle without polarity determination and the polarity of the initial estimated rotor position angle.

[0063] When the permanent magnet synchronous motor is at rest and low speed, a high-frequency pulse square wave signal injection module is used to obtain an accurate estimate of the rotor position angle.

[0064] An unscented Kalman filter extended algorithm module in the form of additive noise includes: a nonlinear discrete dynamic system model unit and a filter extension unit connected in sequence;

[0065] The nonlinear discrete dynamic system model unit of the permanent magnet synchronous motor is used to incorporate the influence of various types of noise into the system state variables. The uncertain factors that cause thrust changes are equivalent to process evolution disturbance noise, referred to as process noise for short. The measurement noise and the remaining noise except the process evolution disturbance noise are regarded as additive noise, and the system state variables are expanded. The first-order discretized state equation is established, and the nonlinear discrete dynamic system model is constructed based on the first-order discretized state equation and the dynamic matrix equation.

[0066] The filter extension unit performs the unscented Kalman filter extension algorithm on the nonlinear discrete dynamic system model to obtain the optimal estimate of the state variable.

[0067] In the medium-speed, high-speed and ultra-high-speed states, the extended unscented Kalman filter algorithm in the form of additive noise fully considers the influence of process evolution noise, system noise and measurement noise, and uses the estimated initial position of the rotor position angle as input to filter the nonlinear discrete dynamic system model using the extended unscented Kalman filter algorithm, including state update, observation update, state correction and other processes, to obtain the optimal estimated observation value of the system state variable, including the optimal estimated observation value rotor position angle

[0068] The fusion unit fuses the estimated rotor position angle output by the high-frequency pulse square wave signal injection module with the optimal estimated observation rotor position angle output by the unscented Kalman filter extended algorithm module in the form of additive noise to obtain the final estimated rotor position angle and determine the rotor position for precise control of the operation of the permanent magnet synchronous motor.

[0069] Example 2:

[0070] A position sensorless control method for a permanent magnet synchronous motor in full speed range of this embodiment can be applied to electronic or electrical equipment with communication, computing, and data storage capabilities, including:

[0071] Sampling the three-phase current i of the permanent magnet synchronous motor in the three-phase abc natural coordinate system a 、i b 、i c， Perform coordinate system conversion to convert the three-phase current of the three-phase abc natural coordinate system to the current i of the dq estimated synchronous rotating coordinate system d 、i q, as the subsequent observation parameter, referred to as observation current.

[0072] Using the observed current as the observed quantity, a high-frequency pulse square wave signal injection method is used in the estimated synchronous rotating coordinate system to obtain the initial estimated rotor position angle. The high-frequency DC injection method is used to calculate the absolute value of the response amplitude of the injected positive d-axis high-frequency DC and the absolute value of the response amplitude of the injected negative d-axis high-frequency DC. The polarity of the initial estimated rotor position angle is determined and the initial estimated rotor position angle is corrected according to the polarity to obtain the estimated rotor position angle. This serves as the main parameter basis for the subsequent estimated rotor position angle at rest or low speed.

[0073] Based on the initial value of the estimated rotor position angle, the unscented Kalman filter extended algorithm in the form of additive noise is used to filter the nonlinear discrete dynamic system model, and the optimal estimated observation values ​​of the system state variables are calculated. The optimal estimated observation values ​​of the system state variables include the optimal estimated observation value of the rotor position angle.

[0074] The estimated rotor position angle and the optimal estimated observed rotor position angle are fused to obtain the final estimated rotor position angle, which is used to determine the rotor position.

[0075] The estimated initial position of the rotor position angle is used as the filter parameter initialization input of the unscented Kalman filter extended algorithm in the form of additive noise. The nonlinear discrete dynamic system model is filtered and the optimal estimated observation value of the system state is obtained.

[0076] According to the optimal estimated observation value of the system state, compensation and feedback are performed on the permanent magnet synchronous motor drive control system adjustment strategy to improve the drive control system adjustment strategy.

[0077] An improved drive control system adjustment strategy is adopted to realize a position sensorless control method for the permanent magnet synchronous motor in the full speed range.

[0078] A high-frequency pulse square wave signal injection method includes: establishing a high-frequency voltage model in an estimated synchronous rotating coordinate system, obtaining an initial estimated rotor position angle based on the injected high-frequency pulse square wave signal, obtaining the polarity of the initial rotor position angle based on high-frequency direct current injection, and correcting the initial estimated rotor position angle using the polarity to obtain an estimated rotor position angle.

[0079] An extended unscented Kalman filter algorithm in the form of additive noise includes: classifying noise into process evolution disturbance noise and additive noise, establishing a general first-order discretized state equation of a permanent magnet synchronous motor, setting system state variables and dynamic matrix equations, constructing a more general nonlinear discrete dynamic system model of the permanent magnet synchronous motor based on the first-order discretized state equation and the dynamic matrix equation, calculating the nonlinear discrete dynamic system model using an extended unscented Kalman filter algorithm in the form of additive noise, obtaining an optimal estimated observation value of the system state, wherein the optimal estimated observation value of the rotor position angle is included in the optimal estimated observation value of the system state.

[0080] The estimated rotor position angle is obtained by position fusion of the estimated rotor position angle and the optimal estimated observation rotor position angle, thereby realizing smooth switching between the high-frequency pulse square wave signal injection method and the unscented Kalman filter extended algorithm in the form of additive noise.

[0081] The optimal estimated observation value of the system state is used to compensate and feedback the adjustment strategy of the permanent magnet synchronous motor drive control system, improve the disturbance of the load torque and eliminate the noise and disturbance introduced by the three-phase current measurement.

[0082] Example 3:

[0083] This embodiment further illustrates the high-frequency pulse square wave signal injection method in the first embodiment.

[0084] The high-frequency voltage model equation of the permanent magnet synchronous motor linear dynamic system in the estimated synchronous rotating coordinate system is shown as follows:

[0085]

[0086] Where,

[0087]

[0088]

[0089] Where, It represents the estimated high-frequency voltage of the dq axis of the synchronous rotating coordinate system, It represents the estimated high-frequency current of dq axis in synchronous rotating coordinate system, represents the difference between the estimated rotor position angle and the true rotor position angle, θ r represents the motor rotor position angle, represents the estimated rotor position angle, represents the cross saturation angle, L dh Indicates the d-axis incremental self-inductance, L qh Indicates the q-axis incremental self-inductance, L dqh Indicates the dq axis cross-coupling incremental inductance, L saIndicates the sum and average value of the dq axis incremental self-inductance, L sd Indicates the average difference of the dq axis incremental self-inductance, L p Represents the positive sequence inductance, L n represents the negative sequence inductance, and p represents the differential operator.

[0090]

[0091]

[0092] The high-frequency pulse square wave injection signal expression is as follows:

[0093]

[0094] Where n represents the number of high-frequency pulse square wave voltages injected, V h is the amplitude of the injected high-frequency pulse square wave signal.

[0095] Under the high-frequency pulse square wave injection, the initial estimated rotor position angle is estimated The steps include:

[0096] S1: sampling stator three-phase current i a 、i b 、i c , transform to the estimated rotor synchronous rotating coordinate system and obtain the observed current

[0097] S2: The observed current is filtered by a high-pass filter to obtain a high-frequency current

[0098] S3: Transform the current change between the measurement points before and after each high-frequency pulse square wave signal is injected into the estimated rotor synchronous rotating coordinate system to obtain the dq axis high-frequency current response amplitude as follows:

[0099]

[0100] Where ΔT is the period of the injected high-frequency pulse square wave signal.

[0101] The current difference between the two sampling time points can be expressed as:

[0102]

[0103] S4: After obtaining the transformation amount of the high-frequency current, the error signal f(Δθ r ′):

[0104]

[0105] The error signal f(Δθ r ′) is adjusted to zero to obtain the initial estimated rotor position angle after convergence without polarity determination

[0106] Initial estimated rotor position angle polarity judgment, including:

[0107] A high-frequency DC current is injected into the d-axis of the estimated synchronous rotating coordinate system, and the polarity of the rotor position angle is determined by the d-axis high-frequency current response. Based on the polarity, the initial estimated rotor position angle is corrected to obtain the estimated rotor position angle.

[0108] Determining the polarity of the rotor position angle includes calculating the effect of the incremental self-inductance change of the dq axis on the high-frequency current, injecting different DC currents into d to perform an initial estimation of the polarity of the rotor position angle, and includes the following steps:

[0109] B1: Assume Δθ r ' is small enough, from formula (8) we can know:

[0110]

[0111] Define the inductance parameters under positive and negative d-axis currents: Positive d-axis current Indicates the d-axis incremental self-inductance L caused by the magnetic saturation of the stator core dh Decrease), negative d-axis current Indicates the d-axis incremental self-inductance L caused by the magnetic desaturation of the stator core dh Increase, where The superscript + sign indicates that the incremental self-inductance L dh The changing trend is increasing. The superscript - sign indicates that the incremental self-inductance L is dh The changing trend is decreasing.

[0112] B2: Calculate the effect of the dq axis incremental self-inductance change on L p The impact of because And L p Molecular linear dependence L dh ,so Positive sequence inductance The superscript + sign indicates that under the positive d-axis current transformation, the positive sequence inductance L p The changing trend is increasing. The superscript - in the symbol indicates that under the positive d-axis current transformation, the positive sequence inductance L p The changing trend is decreasing.

[0113] B3: Calculate the effect of the dq axis incremental self-inductance change on the high-frequency current. Substituting into formula (11), we get:

[0114]

[0115]

[0116] because so

[0117] B4: Polarity determination:

[0118] Polarity judgment is performed by injecting different DC currents. Its physical essence is that when the d-axis is aligned with the N pole of the rotor, the positive d-axis current becomes more saturated, the inductance decreases, and the current response amplitude increases.

[0119] Injecting positive d-axis current to measure high-frequency current response amplitude Inject negative d-axis current to measure the corresponding amplitude of high-frequency current like The estimated rotor position angle obtained in step S4 is The direction is correct; if Then the estimated rotor position angle obtained in step S4 should be corrected to

[0120] Specifically, in B4, the polarity of the initially estimated rotor position angle is determined, as Figure 4 As shown, the following steps are included:

[0121] C1: Polarity-free initial position estimation:

[0122] Injection dref =0A,i qref =0A, obtain

[0123] C2: Positive DC current injection:

[0124] Injection current i dref =2A,i qref =0A, obtain

[0125] C3: Continue to use 0A DC current injection:

[0126] i dref =0A,i qref =0A.

[0127] To A + Compare with A0, if A +>A0, the estimated rotor position obtained by the high-frequency pulse square wave voltage injection method is accurate, that is, the current d-axis is aligned with the rotor N pole; otherwise, the current d-axis is aligned with the rotor S pole, and the estimated rotor position needs to be corrected, that is,

[0128] Using steps S1 to S4, the initial estimated rotor position angle is obtained. Steps B1 to B4 are used to obtain the polarity of the initial estimated rotor position angle, and the estimated rotor position angle is obtained based on the initial estimated rotor position angle and its polarity.

[0129] Repeat the above process to obtain a continuously updated estimated rotor position angle.

[0130] Example 4:

[0131] This embodiment further illustrates the extended algorithm of the unscented Kalman filter in the form of additive noise in the first embodiment.

[0132] No linearization assumption is made for the strongly nonlinear dynamic equations of the permanent magnet synchronous motor; the influence of various types of noise is incorporated into the system state variables, and the uncertain factors that cause thrust changes are equivalent to process noise. The noise that causes thrust changes includes load resistance changes, friction disturbances, etc.; measurement noise and other types of noise except process evolution disturbance noise are all additive noise.

[0133] The process evolution disturbance noise is added to the system state variables to expand the system state variables.

[0134] The general first-order discretized state equation of the permanent magnet synchronous motor is:

[0135]

[0136] Where ω represents the electrical angular velocity, p represents the number of pole pairs, and ψ f represents the permanent magnet flux, v d represents the d-axis voltage, v q represents the q-axis voltage, i d represents the d-axis current, i q represents the q-axis current, L d Indicates the d-axis inductance, L q represents the q-axis inductance, J represents the moment of inertia, f represents the viscous friction coefficient, C ch Indicates the load torque, w c Represents process noise, which is considered as zero-mean Gaussian white noise. e represents electromagnetic torque, and Rs represents the resistance of the motor winding.

[0137] The system state variable is x=[i d i q ω θ C ch] T , the dynamic matrix equation is:

[0138]

[0139] The load torque process noise term in formula (14) is processed separately and is characterized by the process noise covariance matrix.

[0140] According to formulas (14) and (15), a more general nonlinear discrete dynamic system model of permanent magnet synchronous motor can be constructed:

[0141]

[0142] Where, Indicates the reference input voltage, y k =[i d,k i q,k ] T Represents the observation output, w k-1 represents the system noise at time k-1, w c,k-1 represents the k-1 moment process noise, v k represents the observation noise at time k, f(x k-1 ,u k-1 ,w c,k-1 ) represents the system dynamic matrix, h(x k ) represents the output matrix, and k represents the time.

[0143] The continuous state is converted into a discrete state and associated with the sampled current value, thereby improving the accuracy of the calculation.

[0144] The extended algorithm of unscented Kalman filter in the form of additive noise, such as Figure 3 As shown, the following steps are included:

[0145] A1. Initialize filter parameters: take time 0 as the initial time,

[0146]

[0147] P represents the standard deviation.

[0148] A2. Prior prediction sampling of state variables:

[0149] Assume k = 1, 2, 3, ... N, ..., ∞, and take the Sigma point set and its weight coefficient at time k-1 as:

[0150]

[0151] Where α is a scale factor with a value range of 0<α<1, which reduces the influence of possible high-order terms and prevents the covariance matrix from becoming a non-positive definite matrix; κ is a size factor used to control the influence of high-order terms on the approximation results, which can effectively reduce the size of the estimation error; λ is another size factor that satisfies λ=α 2 (n+κ)-n; β is a non-negative weighting factor, the purpose of which is to include the high-order moment information of the state variable in the Sigma point. For the Gaussian prior distribution, β=2 is the optimal choice.

[0152] A3. Update the state variable time of the nonlinear dynamic system equation, then:

[0153] χ i,k / k-1 =f(χ i,k-1 / k-1 ),i=0,1,2,…2n, (18);

[0154] Get the one-step prediction of the system state vector:

[0155]

[0156] One-step prediction error variance matrix of the system state vector:

[0157]

[0158] The corresponding Sigma point update value is:

[0159]

[0160] A4. Update the observation equation, then:

[0161] Y i,k / k-1 =h(χ i,k / k-1 ),i=0,1,2,…,2n, (22);

[0162] Get the one-step forecast for the observation vector:

[0163]

[0164] One-step forecast error variance matrix of the observation vector:

[0165]

[0166] The cross covariance matrix of the system state vector and the observation vector is:

[0167]

[0168] A5. Modify the prediction vector. The corresponding Kalman gain matrix is:

[0169]

[0170] The estimated value of the system state vector is:

[0171]

[0172] The corresponding estimation error matrix is:

[0173]

[0174] Through steps A1--A5, the optimal estimated values ​​of the system state variables and corresponding parameter information such as filtering error variance are obtained.

[0175] Repeat the steps of the unscented Kalman filter extension algorithm in the form of additive noise to obtain the optimal estimate of the state variables:

[0176]

[0177] Includes: Optimal estimated current value and Optimal estimated load torque Optimal estimated observation rotor position angle is the optimal estimated speed.

[0178] The estimated rotor position angle in the second embodiment is compared with the optimal estimated observed rotor position angle in this embodiment, that is, Position fusion is performed to achieve smooth switching between the high-frequency pulse square wave signal injection method and the unscented Kalman filter extended algorithm in the form of additive noise.

[0179] The fusion strategy of the estimated rotor position angle and the optimal estimated observed rotor position angle is as follows:

[0180]

[0181] Where μ is the weighting factor, which is calculated and determined as follows:

[0182]

[0183] Where, is the optimal estimated speed, ω low represents the low-speed boundary of the switching region, ω high Indicates the high-speed boundary of the switching region.

[0184] Using the optimal estimated observation value of the system state variable, the adjustment strategy of the permanent magnet synchronous motor drive control system is compensated and fed back to improve the disturbance of the load torque and eliminate the noise and disturbance introduced by the three-phase current measurement, including: the optimal estimated load torque in the optimal estimated observation value of the system state variable is converted into The input reference torque is compensated after passing through a low-pass filter; the optimal estimated current value in the optimal estimated observation value of the system state variable is converted into Feedback to the current regulator or multi-step hybrid control unit prevents measurement noise from being re-injected into the current regulator, or corrects the operating intention as a target operating point.

[0185] Embodiment 5:

[0186] A control system adjustment strategy for a permanent magnet synchronous motor without a position sensor in full speed range in this embodiment is as follows: Figure 5 As shown, the optimal drive control system is used.

[0187] The operation, control, observation and adjustment component includes a high-frequency pulse square wave signal injection module, an unscented Kalman filter extended algorithm module in the form of additive noise, a fusion module, a high-pass filter, a first coordinate converter and an optimal drive system control strategy module. The first coordinate converter is used to convert the abc natural coordinate system into a dq estimated synchronous rotating coordinate system to perform high-frequency pulse square wave signal injection operations and unscented Kalman filter extended algorithm operations in the form of additive noise.

[0188] The optimal drive system control strategy module includes a speed regulator, a current and torque relationship solving module, a current regulator and a second coordinate converter connected in sequence. The second coordinate converter is used to convert the dq estimated synchronous rotating coordinate system into the abc natural coordinate system. The output of the second coordinate converter is connected to the drive inverter to transmit the parameters converted into the natural coordinate system to the drive inverter to control the operation of the permanent magnet synchronous motor.

[0189] The full-speed range control system adjustment strategy is as follows:

[0190] Sampling the three-phase current i of the permanent magnet synchronous motor a 、i b 、i c， After the first coordinate converter, the three-phase current in the abc coordinate system is converted into the dq current i in the dq coordinate system. d 、i q .

[0191] High-frequency pulse square wave signal injection module, according to the dq current i d 、i q， After passing through the linearized high-frequency motor model unit, demodulation unit, and position observer unit, the initial estimated rotor position angle is obtained. After passing through the polarity judgment unit, the polarity of the initial estimated rotor position angle is obtained. After passing through the adjustment unit, the initial estimated rotor position angle is adjusted according to the polarity of the initial estimated rotor position angle to obtain the estimated rotor position angle, which is then transmitted to the fusion module.

[0192] The extended algorithm module of the unscented Kalman filter in the form of additive noise is based on the dq current i d 、i q， The Kalman filter extended algorithm is used to obtain the optimal estimated observation value of the system state.

[0193] The position fusion module obtains the final estimated rotor position angle based on the estimated rotor position angle and the optimal estimated rotor position angle among the optimal estimated observation values ​​of the system state, transmits it to the first coordinate converter for feedback of the rotor position, and transmits it to the second coordinate converter for combining with the optimal estimated observation values ​​of the system state variables to control the operation of the drive inverter.

[0194] The workflow of the optimal drive system control strategy module is as follows:

[0195] The optimal estimated speed in the optimal estimated observation value of the system state The value, combined with the pole pair number, obtains the optimal estimated rotor mechanical rotation speed value Or take the derivative of the final estimated rotor position angle to obtain the optimal estimated rotor mechanical rotation speed value

[0196] The optimal estimated rotor mechanical rotation speed value Combined with the input Ω of the operation control observation and regulation component, it is input into the speed regulator, and the speed regulator outputs the torque signal.

[0197] Load torque in the optimal estimated observation value of system state After passing through the low-pass filter, the torque signal output by the speed regulator is compensated and transmitted to the current and torque relationship solving module. After the calculation of the current and torque relationship solving module, the dq current reference value i is obtained. dref 、i qref .

[0198] The dq current reference value i dref The optimal estimated current value in the optimal estimated observation value of the system state After the combination, the feedback is given to the current regulator, and the dq current reference value i qref The optimal estimated current value in the optimal estimated observation value of the system state After the combination, the feedback is given to the current regulator to avoid the measurement noise from being re-injected into the current regulator. The current regulator outputs the voltage reference value of the dq coordinate system according to the modified current parameters.

[0199] The voltage reference value of the dq coordinate system and d-axis high frequency voltage After the combination, it is transmitted to the second coordinate converter and fed back to the unscented Kalman filter extended algorithm module in the form of additive noise to convert the voltage reference value of the dq coordinate system into The data are transmitted to the second coordinate converter and fed back to the unscented Kalman filter extended algorithm module in the form of additive noise. The second coordinate converter converts the control parameters in the dq coordinate system into control parameters in the natural coordinate system and transmits them to the switch trigger timing module of the drive inverter to control the switches of multiple groups of power semiconductor devices of the drive inverter, thereby controlling the operation of the permanent magnet synchronous motor and realizing efficient and precise control operation of the permanent magnet synchronous motor.

[0200] Through the above feedback and compensation, the disturbance of the load torque is improved and the noise and disturbance introduced by the three-phase current measurement are eliminated.

[0201] The speed regulator and current regulator both adopt PID regulation and limiting control strategies.

[0202] The optimal dq current and torque relationship solving module operation modes include the following:

[0203] Mode 1 is the maximum electromagnetic torque operation under current limiting conditions, that is, the maximum electromagnetic torque control strategy is generated per ampere of armature current;

[0204] Mode 2 is the maximum electromagnetic power operation under current and voltage limitation conditions, i.e., the flux weakening speed expansion control strategy;

[0205] Mode three is the operation of maximum electromagnetic power under voltage limitation conditions, that is, the maximum torque-voltage ratio control strategy.

[0206] The relationship between current and torque in the above three modes can be expressed in a lookup table to improve the dynamic response capability of the system.

[0207] Example 6:

[0208] A control system adjustment strategy for a permanent magnet synchronous motor without a position sensor in full speed range in this embodiment is as follows: Figure 6 As shown, a drive control system is adopted with the operation intention as the target operating point.

[0209] The difference from the fifth embodiment is that the optimal drive system control strategy module in the fifth embodiment is replaced by a drive system control strategy module that uses the operation intention as the target operating point, and the rest of the structure is the same as the fifth embodiment.

[0210] The drive system control strategy module with the operation intention as the target operating point includes a speed regulator, a current and torque relationship solving module, a multi-step hybrid control unit module, and a timing positioning control unit module connected in sequence.

[0211] The workflow of the drive system control strategy module with the operation intention as the target operating point is as follows:

[0212] The best estimate among the best estimate observations of the system state Combined with the input Ω of the operation control observation and regulation component, it is input into the speed regulator, and the speed regulator outputs the torque signal.

[0213] Load torque in the optimal estimated observation value of system state After passing through the low-pass filter, the filtered load torque is obtained. After compensating the torque signal output by the speed regulator, it is transmitted to the current and torque relationship solving module. After the calculation of the current and torque relationship solving module, the dq current reference value i is obtained. dref 、i qref .

[0214] The dq current reference value i dref The optimal estimated current value in the optimal estimated observation value of the system state After the combination, the feedback is given to the multi-step hybrid control unit module, and the dq current reference value i qref The optimal estimated current value in the optimal estimated observation value of the system state After the combination, the feedback is given to the multi-step hybrid control unit module to avoid the measurement noise from being re-injected into the multi-step hybrid control unit module. The multi-step hybrid control unit module outputs the voltage reference value of the dq coordinate system according to the modified current parameters. Extends the algorithm module of the unscented Kalman filter to the additive noise form.

[0215] The multi-step hybrid control unit and the switching state timing positioning control unit of the power semiconductor devices on each bridge arm of the inverter are used to dref 、i qref As the target operating point, the multi-step hybrid control unit determines that the configuration selections i and j are different in only one switching state of the power semiconductor device on the inverter bridge arm, combined with the action time τ i , τ j , τ7, and determines the switching state of the power semiconductor devices on each bridge arm of the drive inverter through the state timing positioning control unit, thereby realizing efficient and precise control operation of the permanent magnet synchronous motor.

[0216] Embodiment seven:

[0217] Another embodiment of the present application relates to an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute a position sensorless control method for a permanent magnet synchronous motor in full speed range according to each of the above embodiments.

[0218] The memory and processor are connected using a bus, which can include any number of interconnected buses and bridges. The bus connects various circuits of one or more processors and memories. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits. These are all well known in the art and are therefore not described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single component or multiple components, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over a wireless medium via an antenna. Furthermore, the antenna receives data and transmits it to the processor.

[0219] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory can be used to store data used by the processor when performing operations.

[0220] Embodiment 8:

[0221] Another embodiment of the present application relates to a computer-readable storage medium storing a computer program, which implements the above method embodiment when executed by a processor.

[0222] That is, those skilled in the art will understand that all or part of the steps in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a program, which is stored in a storage medium and includes a number of instructions for causing a device (which may be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., various media that can store program code.

[0223] Those skilled in the art will appreciate that the above embodiments are specific embodiments for implementing the present application, and that in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present application.

Claims

1. A method for controlling a permanent magnet synchronous motor without a position sensor in the full speed range, characterized in that: include: The three-phase current of the permanent magnet synchronous motor in the natural coordinate system is sampled and the coordinate system is transformed to obtain the observed current in the synchronous rotating coordinate system. Based on the observed current, a high-frequency pulse square wave signal injection strategy is adopted to determine the initial estimated rotor position angle under the high-frequency pulse square wave signal injection; determining an initial estimated rotor position polarity using a high-frequency current response amplitude under high-frequency current injection, and determining an estimated rotor position angle based on the rotor position polarity and the initial estimated rotor position angle; An extended unscented Kalman filter algorithm in the form of additive noise is used to obtain the optimal estimated observation value of the system state based on the initial position of the estimated rotor position angle. The optimal estimated observation value of the system state includes the optimal estimated observation rotor position angle. The estimated rotor position angle and the optimal estimated observation rotor position angle are fused to obtain the final estimated rotor position angle. The final estimated rotor position angle and the optimal estimated observation value of the system state are fed back to the permanent magnet synchronous motor drive control system regulation system to improve the control of the permanent magnet synchronous motor.

2. A sensorless control method for a permanent magnet synchronous motor in full speed range according to claim 1, characterized in that: The method of using a high-frequency pulse square wave signal injection to determine an initial estimated rotor position angle includes: establishing a linearized high-frequency voltage model in an estimated synchronous rotating coordinate system, and obtaining a high-frequency current based on an observed current; estimating a relationship between a q-axis current transformation amount and an initial estimated rotor position angle error in the estimated synchronous rotating coordinate system based on a current difference between measurement points before and after each high-frequency pulse square wave signal injection, obtaining an error signal, and converging the error signal to obtain an initial estimated rotor position angle.

3. A position sensorless control method for a permanent magnet synchronous motor in full speed range according to claim 2, characterized in that: The method of using the high-frequency current response amplitude under high-frequency current injection to determine the initial estimated rotor position polarity includes: defining inductance parameters under positive and negative d-axis currents; calculating the impact of d / q axis incremental self-inductance changes on positive-sequence inductance and high-frequency current, and determining the polarity of the initial estimated rotor position angle based on the absolute value of the high-frequency current response amplitude when the positive d-axis current is injected and the absolute value of the high-frequency current response amplitude when the negative d-axis current is injected.

4. The method for controlling a permanent magnet synchronous motor in full speed range without a position sensor according to claim 1, characterized in that: The unscented Kalman filter extended algorithm using additive noise obtains the optimal estimated observation value of the system state based on the estimated initial position of the rotor position angle, including: establishing a nonlinear discrete dynamic system model of the permanent magnet synchronous motor, initializing the filter parameters, obtaining the Sigma point set and its weight coefficients at the previous moment, updating the time of the nonlinear discrete dynamic system model, updating the observation equation, obtaining the one-step observation value of the system state, correcting the prediction vector, and obtaining the optimal system state variable.

5. A position sensorless control method for a permanent magnet synchronous motor in full speed range according to claim 4, characterized in that: The nonlinear discrete dynamic system model of the permanent magnet synchronous motor is established as shown in the following formula: Where, Indicates the reference input voltage, y k =[i d,k i q,k ] T Represents the observation output, w k-1 represents the system noise, w c,k-1 represents process noise, v k represents the observation noise, f(x k-1 ,u k-1 ,w c,k-1 ) represents the system dynamic matrix, h(x k ) represents the output matrix, K represents the time, x=[i d i q ωθC ch ] T Represents system state variables.

6. The method for controlling a permanent magnet synchronous motor in full speed range without position sensor according to claim 1, characterized in that: The estimated rotor position angle and the optimal estimated observation rotor position angle are fused to obtain the final estimated rotor position angle, including: the position fusion strategy of the estimated rotor position angle and the optimal estimated observation rotor position angle is shown in the following formula: In the formula, μ represents the weighting factor, which is calculated as follows: in, is the optimal estimated speed, ω low is the low-speed boundary of the switching area, ω high It is the high-speed boundary of the switching area.

7. A full-speed range position sensorless control system for a permanent magnet synchronous motor, characterized in that: include: Nonlinear dynamic hybrid components and computational control observation and regulation components; The operation control observation and adjustment component includes: a high-frequency pulse square wave signal injection module, an unscented Kalman filter extended algorithm module in the form of additive noise, and a fusion module. The high-frequency pulse square wave signal injection module and the unscented Kalman filter extended algorithm module in the form of additive noise are respectively connected to the fusion module, and are used to adopt the full-speed domain control method according to any one of claims 1 to 6 to output a final estimated rotor position angle and an optimal estimated observation value of a system state, determine the rotor position by using the final estimated rotor position angle, improve the parameters of the drive control system adjustment strategy by using the optimal estimated observation value of the system state, and control the permanent magnet synchronous motor to operate without a position sensor within the full speed domain; A fusion module is used to fuse the estimated rotor position angle output by the high-frequency pulse square wave signal injection module and the optimal estimated observation rotor position angle output by the unscented Kalman filter extended algorithm module in the form of additive noise, and obtain the final estimated rotor position angle; An extended algorithm module for the unscented Kalman filter in the form of additive noise is used to output the optimal estimated observation value of the system state based on the current of the permanent magnet synchronous motor in the rotating coordinate system; The nonlinear dynamic hybrid component includes a drive inversion module, which is used to correct the drive parameters of the permanent magnet synchronous motor according to the optimal estimated observation value of the system state and output a drive signal to the permanent magnet synchronous motor.

8. A permanent magnet synchronous motor full speed range position sensorless control system according to claim 7, characterized in that: The high-frequency pulse square wave signal injection module includes: a linearized high-frequency motor model unit, a demodulation unit, a position observer unit, a polarity judgment unit and an adjustment unit, wherein the linearized high-frequency motor model unit, the demodulation unit and the position observer unit are connected in sequence, and the position observer unit and the polarity judgment unit are respectively connected to the adjustment unit; A linearized high-frequency motor model unit is used to sample the three-phase current of the permanent magnet synchronous motor based on the linearized high-frequency motor model and the high-frequency pulse square wave injection signal, and perform coordinate system conversion to obtain the observed current in the estimated synchronous rotating coordinate system, and obtain the high-frequency current after filtering the observed current; A demodulation unit is used to estimate the relationship between the q-axis current transformation amount estimated in the synchronous rotating coordinate system and the initial estimated rotor position angle error based on the current difference between the measurement points before and after each high-frequency pulse square wave signal is injected, so as to obtain an error signal; A position observer unit is used to adjust the error signal to zero to obtain an initial estimated rotor position angle after convergence without polarity determination; a polarity determination unit, configured to calculate a high-frequency current response amplitude when a positive d-axis current is injected, and a high-frequency current response amplitude when a negative d-axis current is injected, and obtain the polarity of an initial estimated rotor position angle based on a comparison of the absolute value of the high-frequency current response amplitude when the positive d-axis current is injected and the absolute value of the high-frequency current response amplitude when the negative d-axis current is injected; The adjustment unit is configured to obtain an estimated rotor position angle according to the initial estimated rotor position angle and the polarity of the initial estimated rotor position angle.

9. A permanent magnet synchronous motor full speed range position sensorless control system according to claim 7, characterized in that: The unscented Kalman filter extended algorithm module in the additive noise form includes: a nonlinear discrete dynamic system model unit and a filter extension unit connected in sequence; The nonlinear discrete dynamic system model unit is used to consider the influence of various types of noise and incorporate the expansion of system state variables. Various types of noise include process evolution disturbance noise, additive noise, and observation noise. The first-order discretized state equation is established, and the nonlinear discrete dynamic system model is constructed based on the first-order discretized state equation and the dynamic matrix equation. The filter extension unit is used to perform an unscented Kalman filter extension algorithm on the nonlinear discrete dynamic system model to obtain the optimal estimated observation value of the system state variable.

10. An electronic device, characterized in that: include: at least one processor; as well as, a memory connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a position sensorless control method for a permanent magnet synchronous motor in full speed range as described in any one of claims 1 to 6.

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